Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules |
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Authors: | Mehmet Kaya |
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Affiliation: | (1) Department of Computer Engineering, Firat University, 23119 Elazig, Turkey |
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Abstract: | Association rules form one of the most widely used techniques to discover correlations among attribute in a database. So far,
some efficient methods have been proposed to obtain these rules with respect to an optimal goal, such as: to maximize the
number of large itemsets and interesting rules or the values of support and confidence for the discovered rules. This paper
first introduces optimized fuzzy association rule mining in terms of three important criteria; strongness, interestingness
and comprehensibility. Then, it proposes multi-objective Genetic Algorithm (GA) based approaches for discovering these optimized
rules. Optimization technique according to given criterion may be one of two different forms; The first tries to determine
the appropriate fuzzy sets of quantitative attributes in a prespecified rule, which is also called as certain rule. The second
deals with finding both uncertain rules and their appropriate fuzzy sets. Experimental results conducted on a real data set
show the effectiveness and applicability of the proposed approach. |
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Keywords: | Fuzzy association rules Multi-objective optimization Genetic algorithms |
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